Method and apparatus for testing request-response service using live connection traffic

ABSTRACT

The present invention provides for a method and apparatus for comparison of network systems using live traffic in real-time. The inventive technique presents real-world workload in real-time with no external impact (i.e. no impact on the system under test), and it enables comparison against a production system for correctness verification. A preferred embodiment of the invention is a testing tool for the pseudo-live testing of CDN content staging servers, According to the invention, traffic between clients and the live production CDN servers is monitored by a simulator device, which then replicates this workload onto a system under test (SUT). The simulator detects divergences between the outputs from the SUT and live production servers, allowing detection of erroneous behavior. To the extent possible, the SUT is completely isolated from the outside world so that errors or crashes by this system do not affect either the CDN customers or the end users. Thus, the SUT does not interact with end users (i.e., their web browsers). Consequently, the simulator serves as a proxy for the clients. By basing its behavior off the packet stream sent between client and the live production system, the simulator can simulate most of the oddities of real-world client behavior, including malformed packets, timeouts, dropped traffic and reset connections, among others.

This application is based on Provisional Application Ser. No. 60/189,734, filed Mar. 16, 2000.

This application includes subject matter that is protected by Copyright Law. All rights reserved.

BACKGROUND OF THE INVENTION

1. Technical Field

The present invention relates generally to testing a request-response service using live connection traffic. One such request-response service involves high-performance, fault-tolerant HTTP, streaming media and applications delivery over a content delivery network (CDN).

2. Description of the Related Art

It is well-known to deliver HTTP and streaming media using a content delivery network (CDN). A CDN is a self-organizing network of geographically distributed content delivery nodes that are arranged for efficient delivery of digital content (e.g., Web content, streaming media and applications) on behalf of third party content providers. A request from a requesting end user for given content is directed to a “best” replica, where “best” usually means that the item is served to the client quickly compared to the time it would take to fetch it from the content provider origin server. An entity that provides a CDN is sometimes referred to as a content delivery network service provider or CDNSP.

Typically, a CDN is implemented as a combination of a content delivery infrastructure, a request-routing mechanism, and a distribution infrastructure. The content delivery infrastructure usually comprises a set of “surrogate” origin servers that are located at strategic locations (e.g., Internet network access points, Internet Points of Presence, and the like) for delivering copies of content to requesting end users. The request-routing mechanism allocates servers in the content delivery infrastructure to requesting clients in a way that, for web content delivery, minimizes a given client's response time and, for streaming media delivery, provides for the highest quality. The distribution infrastructure consists of on-demand or push-based mechanisms that move content from the origin server to the surrogates. An effective CDN serves frequently-accessed content from a surrogate that is optimal for a given requesting client. In a typical CDN, a single service provider operates the request-routers, the surrogates, and the content distributors. In addition, that service provider establishes business relationships with content publishers and acts on behalf of their origin server sites to provide a distributed delivery system. A well-known commercial CDN service that provides web content and media streaming is provided by Akamai Technologies, Inc. of Cambridge, Mass.

CDNSPs may use content modification to tag content provider content for delivery. Content modification enables a content provider to take direct control over request-routing without the need for specific switching devices or directory services between the requesting clients and the origin server. Typically, content objects are made up of a basic structure that includes references to additional, embedded content objects. Most web pages, for example, consist of an HTML document that contains plain text together with some embedded objects, such as .gif or .jpg images. The embedded objects are referenced using embedded HTML directives. A similar scheme is used for some types of streaming content which, for example, may be embedded within an SMIL document. Embedded HTML or SMIL directives tell the client to fetch embedded objects from the origin server. Using a CDN content modification scheme, a content provider can modify references to embedded objects so that the client is told to fetch an embedded object from the best surrogate (instead of from the origin server).

In operation, when a client makes a request for an object that is being served from the CDN, an optimal or “best” edge-based content server is identified. The client browser then makes a request for the content from that server. When the requested object is not available from the identified server, the object may be retrieved from another CDN content server or, failing that, from the origin server.

A well-managed content delivery network implements frequent upgrades to its production software, e.g., the software used to provide HTTP content delivery from its edge-based content servers. Thus, for example, as new content or “edge” server functionalities are added to the network, they need to be tested, debugged, rewritten and, ultimately, deployed into production across the network as a whole. An ongoing challenge is testing such new software is the inability to reproduce real-world workload on new versions of the software short of deploying them in the field. While testing a CDN server with real-world traffic (a “live load test”) would be desirable, it has not been possible to do so without having the CDN server interact with the outside world. This interaction may cause significant problems if the version under live test has bugs or otherwise interferes with conventional server functions. Additionally, when field-deployment is used, there is no convenient mechanism for checking if a new version of the software under test produces equivalent output to the old version, namely, the production version.

Generally, there are a number of known approaches to testing software. Regression testing refers to the technique of constructing test cases and executing the software against those cases. Regression testing, while effective in avoiding repeat of bugs, is labor-intensive and thus costly. Stress or “load” testing refers to the technique of simulating the working environment of the software using a testbed or equivalent architecture. While stress/load testing is useful in evaluating system limits, finding representative workloads to use for the test is always difficult. Trace-based testing refers to the technique of playing back to the software under test a trace of activity obtained from a production version. This technique, although generally useful, may lead to inaccurate conclusions as, in some applications (like a CDN caching server), traces go stale very quickly and/or do not include information that might be needed to evaluate the new version effectively. Field-deployment testing, as its name suggests, refers to the technique of testing a version of the software with a real-world workload. As noted above, when field-deployment is used, there is no convenient way of isolating the software under test from interacting with real users and customers, and there is no mechanism for checking if a new version of the software under test produces equivalent output to the old version, namely, the production version. Error detection is hard, and debugging is difficult because there is limited information capture and the developer is often unable to deploy instrumented code. In addition, during live field-testing, the developer is not able to destructively test the code, i.e., to make the software less robust (e.g., letting it crash) in the face of problems instead of patching over them, in order to assist in tracking down problems.

It would be desirable to be able to provide a way to test IP-networking-based servers (either software, hardware, or some combination thereof) with live traffic and to compare the results of these tests with currently running CDN traffic. Such a method also could be used to test network-based servers before their actual deployment. The present invention addresses this need in the art.

BRIEF SUMMARY OF THE INVENTION

The present invention provides for a method and apparatus for comparison of network systems using live traffic in real-time. The inventive technique presents real-world workload in real-time with no external impact (i.e. no impact on customers of the service, nor the system providing the service), and it enables comparison against a production system for correctness verification.

A preferred embodiment of the invention is a testing tool for the pseudo-live testing of CDN content staging servers, although this is not a limitation of the invention. When deployed, production content staging servers (also referred to as reverse proxies or surrogate origin servers) sit behind a switch providing connectivity to the Internet. These switches often have a port-monitoring feature, used for management and monitoring, which allows all traffic going through the switch to be seen on the configured port. According to the invention, traffic between clients and the live production CDN servers is monitored by a simulator device, which replicates this workload onto a system under test (SUT). The simulator provides high-fidelity duplication (ideally down to the ethernet frame level), while also compensating for differences in the output between the system under test and the live production system. Additionally, the simulator detects divergences between the outputs from the SUT and live production servers, allowing detection of erroneous behavior. To the extent possible, the SUT is completely isolated from the outside world so that errors or crashes by this system do not affect either the CDN customers or the end users. Thus, the SUT does not interact with end users (i.e., their web browsers). Consequently, the simulator serves as a proxy for the clients. By basing its behavior off the packet stream sent between client and the live production system, the simulator can simulate most of the oddities of real-world client behavior, including malformed packets, timeouts, dropped traffic and reset connections, among others.

In a preferred embodiment, the main functionality of the tool is provided by an External World Simulator (EWS). The EWS listens promiscuously on a CDN region switch interface, rewrites incoming client packets bound for a production server to be routed to a beta server being tested, optionally compares the content and headers of the beta reply to the production reply, and black-holes (i.e. terminates) the client bound traffic from the beta server. A primary advantage this tool provides is the ability to put servers of an unknown quality into a live environment and to receive notification if the client experience differs from a known standard (as provided by the production servers).

The simulator may provide varying degrees of validation. Thus, for example, the simulator may provide substantially limited validation that suffices for testing new versions for crashes and long-term memory leaks. The simulator may test for “identical” output, wherein the output of the system under test is checked for byte-for-byte equality with the production system. The simulator may also check for “equivalent” output, wherein the output of the SUT and the production system are checked for logical equivalence (isomorphism). This type of validation typically involves use of specific application-level logic. The particular equivalence checking logic will depend on the functionalities being implemented, of course.

The foregoing has outlined some of the more pertinent features and technical advantages of the present invention. These features and advantages should be construed to be merely illustrative. Many other beneficial results can be attained by applying the disclosed invention in a different manner or by modifying the invention as will be described. Accordingly, other features and a fuller understanding of the invention may be had by referring to the following Detailed Description of the Preferred Embodiment

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a known content delivery network in which the present invention may be implemented;

FIG. 2 is a simplified block diagram of a known CDN content server;

FIG. 3 is a simplified block diagram of how a CDN region may be implemented in the prior art;

FIG. 4 is a block diagram of the inventive live-load testing system infrastructure of the present invention;

FIG. 5 is a block diagram illustrating a preferred architecture of the software modules that comprise the External World Simulator;

FIGS. 6-7 are state diagrams illustrating how the EWS manages (opens and closes) connections between the production ghost(s) and the invisible ghost(s) according to the preferred embodiment; and

FIGS. 8-14 illustrate the operation of the EWS for a given connection between a requesting client and a production server.

DESCRIPTION OF THE PREFERRED EMBODIMENT OF THE INVENTION

FIG. 1 is a diagram showing an illustrative content delivery network in which the present invention may be implemented. The content delivery service comprises a preferably global content delivery network (CDN) 100 of content delivery server regions 102 a-n, a domain name service (DNS) system 104, and a content modification or “initiator” tool 106 that allows content to be tagged for inclusion on the network. DNS system 104 receives network mapping data from a map maker 107, which receives inputs from monitoring agents 109 distributed throughout the Internet. Agents typically perform various tests and monitor traffic conditions to identify Internet congestion problems. The map maker 107 takes the data generated from the agents and generates one or more maps detailing Internet traffic conditions. Generally, the content delivery service allows the network of content delivery server regions 102 a-n to serve a large number of clients efficiently. Each region may include one or more content servers, with multiple content servers typically sharing a local area network (LAN) backbone. Although not meant to be limiting, a typical server is an Intel Pentium-based caching appliance running the Linux operating system with a large amount of RAM and disk storage. As also seen in FIG. 1, the content delivery service may include a network operations control center (NOC) 112 for monitoring the network to ensure that key processes are running, systems have not exceeded capacity, and that subsets of content servers (the so-called CDN regions 102) are interacting properly. A content provider operates an origin server (or server farm) 115 from which requesting end users 119 would normally access the content provider's Web site via the Internet. Use of the CDN avoids transit over the Internet for selected content as described below. The content provider may also have access to a monitoring suite 114 that includes tools for both real-time and historic analysis of customer data.

High-performance content delivery is provided by directing requests for web objects (e.g., graphics, images, streaming media, HTML and the like) to the content delivery service network. In one known technique, known as Akamai FreeFlow content delivery, HTTP and/or streaming media content is first tagged for delivery by the tool 106, which, for example, may be executed by a content provider at the content provider's web site 115. The initiator tool 106 converts URLs that refer to streaming content to modified resource locators, called ARLs for convenience, so that requests for such media are served preferentially from the CDN instead of the origin server. When an Internet user visit's a CDN customer's site (e.g., origin server 115) and, for example, selects a link to view or hear streaming media, the user's system resolves the domain in the ARL to an IP address. In particular, because the content has been tagged for delivery by the CDN, the URL modification, transparent to the user, cues a dynamic Domain Name Service (dDNS) to query a CDN name server (or hierarchy of name servers) 104 to identify the appropriate media server from which to obtain the stream. The CDN typically implements a request-routing mechanism (e.g., under the control of maps generated from the monitoring agents 109 and map maker 107) to identify an optimal server for each user at a given moment in time. Because each user is served from the optimal streaming server, preferably based on real-time Internet conditions, streaming media content is served reliably and with the least possible packet loss and, thus, the best possible quality. Further details of a preferred dDNS-based request-routing mechanism are described in U.S. Pat. No. 6,108,703, which is incorporated herein by reference.

FIG. 2 is a representative CDN content server 200. Typically, the content server 200 is a Pentium-based caching appliance running an operating system kernel 202 (e.g., based on Linux), a file system cache 204, CDN global host (or “ghost”) software 206, TCP connection manager 208, and disk storage 210. CDN ghost software 206 is useful to create a “hot” object cache 212 for popular objects being served by the CDN. In operation, the content server 200 receives end user requests for content, determines whether the requested object is present in the hot object cache or the disk storage, serves the requested object via HTTP (if it is present) or establishes a connection to another content server or an origin server to attempt to retrieve the requested object upon a cache miss. In a CDN such as described above with respect to FIG. 1, a set of CDN content servers may be organized and managed together in a peer-to-peer manner as a CDN region. FIG. 3 illustrates one such CDN region. In this example, which is merely representative, the CDN region comprises two (2) sets of four (4) production servers 300 a-h that are interconnected over a common backnet 302, which may be a conventional ethernet 100BT switch as illustrated. One or more ethernet switches 304 a-b may be used as a front end to interconnect the CDN region to the public Internet 306, an intranet, a virtual private network, or the like. Although not meant to be limiting, the production servers may be architectured as illustrated in FIG. 2 and described above.

A well-managed CDN has production servers that are frequently upgraded and enhanced with new software version. As a CDN grows in size, however, it becomes very difficult to test such new software and/or software versions given the scale of the network, the size of the codebase, the problems and deficiencies associated with laboratory or field-testing that have been discussed above. The present invention addresses this problem through a novel live-load systems testing infrastructure and methodology which are now illustrated and described.

FIG. 4 illustrates an implementation of the testing infrastructure 400 in the context of a CDN region, which is an exemplary application testing environment. In this example, the infrastructure comprises an External World Simulator 402 that sits between the production system and the system under test (SUT) 404. The EWS listens promiscuously on a CDN region switch interface, rewrites incoming client packets bound for a production server to be routed to a beta server being tested, optionally compares the content and headers of the beta reply to the production reply, and black-holes (i.e. terminates) the client bound traffic from the beta server. An advantage this tool provides is the ability to put servers of an unknown quality into a live environment and to receive notification if the client experience differs from a known standard (as provided by the production servers). In this example, the production system is illustrated by the CDN production region comprising four (4) production ghost servers 406 a-d and the ethernet front-end switch 408. The backnet is omitted for clarity. The SUT comprises a set of four (4) so-called “invisible” ghost servers 410 a-d and the front-end switch 412. A backnet may be used as well. Preferably, there is one invisible ghost server under test for every production ghost server, although this is not a requirement. As noted above, the External World Simulator 402 monitors live traffic between the live production system and requesting clients (not shown) and replicates this workload onto the SUT 404. The EWS 402 provides high fidelity duplication (ideally down to the ethernet frame level), while compensating for differences in the output between the SUT and the live production system. Additionally, the EWS detects divergences between the outputs for corresponding pairs of SUT and live production servers (e.g., servers 406 a and 410 a, 406 b and 410 b, etc.), thereby allowing detection of erroneous behavior.

Although FIG. 4 illustrates a SUT with multiple invisible ghosts, this is not a limitation. The number of machines under test is variable, and may include just a single invisible ghost server, a full region of servers (such as illustrated), multiple regions, and the like. In addition, while preferably the infrastructure uses live system load for testing (i.e., CDN traffic is monitored and its traffic replicated in real-time to drive the SUT), a recorded trace may be captured by the EWS and replayed to the SUT at a later time for testing purposes.

The term “invisible” is merely a shorthand reference to the fact that the SUT is completely isolated from the outside world so that errors or crashes by this system do not affect either the CDN's customers (content providers) or end users. In particular, the basic constraint that is enforced is that the SUT never interacts with end users (namely, their web browsers). Consequently, the EWS serves as a proxy for the clients. By basing its behavior off the packet stream sent between clients and the live production system, the External World Simulator can simulate most of the oddities of real-world client behavior including, without limitation, malformed packets, timeouts, dropped traffic and reset connections. Ideally, the SUT is able to emulate all outside entities (e.g., end user web browsers, customer web servers, DNS servers, network time services, and the like) to which the production ghost server talks in a conventional CDN operation.

Although not meant to be limiting, the EWS preferably is a dual NIC, Intel/Linux-based machine running appropriate control routines for carrying out the above-described testing functionality. The production environment may be any commercial or proprietary Internet-, intranet- or enterprise-based content delivery network. An advantage this tool provides is the ability to put servers of an unknown quality into a live environment and to receive notification if the client experience differs from a known standard (as provided by the production servers). The tool may be augmented to allow one to route traffic from multiple production servers at a single test server—enabling a more realistic performance projection tool. In addition, to handle greater throughout, HTTP comparison can be disabled.

EWS enables monitoring of a production system to generate network-packet level accurate traffic. This provides an extremely high-fidelity workload for the test system. The external interaction may be at selectable test levels such as: HTTP request, IP packet, IP packet and timing, IP packet, timing and fragmentation. The EWS preferably handles various protocols, such as HTTP, HTTPS, and the like. The SUT response stream validation can be of varying degrees, such as limited, identical output and/or equivalent output. Thus, for example, the simulator may provide substantially limited validation that suffices for testing new versions for crashes and long-term memory leaks. The simulator may test for “identical” output, wherein the output of the system under test is checked for byte-for-byte equality with the production system. The simulator may also check for “equivalent” output, wherein the output of the SUT and the production system are checked for logical equivalence (isomorphism). This type of validation typically involves use of specific application-level logic (e.g., checking dates in HTTP headers to determine if two different versions of an object being returned to a requesting client are valid comparing the output of persistent multi-GET connection versus several simple GET requests, etc.). The particular equivalence checking logic will depend on the functionalities being implemented, of course. As noted above, the scale of the system under test may be a single server (or given processes or programs running thereon), a full region of servers, multiple regions, and the like, and the testing environment may be used with live system load or with recorded client traces.

FIG. 5 illustrates one possible implementation of the External World Simulator. The EWS 500 comprises a set of software modules: a collector 502, a state machine 504, a logger 506, an emitter 508, and a comparator 510. Preferably, the modules communicate via frame queues and operate in both time-slice and threaded modes of operations. The collector 502 is responsible for acquiring packets from the network, preferably using a sniffing library routine, and it also receives responses from the invisible ghosts (because it is the entry point for the EWS). In particular, and although not meant to be limiting, preferably the collector 502 takes advantage of the port-monitoring feature of existing ethernet switches in the CDN region. The port-monitoring feature, used for management and monitoring, allows all traffic going through the switch to be seen on the configured port. The collector 502 pulls traffic from the switch port-monitor (using the sniffing library), performs filtering for interesting packets (e.g., HTTP traffic on the production ghost server), and then feeds those packets into the state machine 504 and the logger 506. The state machine 504 is the core logic of the EWS. It decides what packets should be sent and when. The state machine opens and closes connections between the participating entities, namely, the client, the production ghost server, and the invisible ghost server, as will be described in more detail below. The state machine also absorbs invisible ghost server responses to ensure that the SUT never interacts with the production servers. In particular, these response packets follow the path through the collector (the input to the EWS), and the state machine recognizes them as client-bound traffic and absorbs them.

As illustrated, the state machine 504 feeds packets into the emitter 508 and the comparator 510. The emitter 508 sends packets onto the network if needed, and isolates the state machine from the other functions. The comparator 510 assembles HTTP requests/responses from the TCP packets. It performs equivalence checking (depending on the application logic included) between the production ghost response and that of the invisible ghost. In one example, the checking verifies that HTTP response codes match. There may be some cases when the codes match but the content handed back (from the respective production ghost and the invisible ghost) differs, or the response code may not match when the content handed back is the same, and so on. The comparator may filter the data based on given criteria. Typically, the comparator writes given data to a log for later analysis. The comparator typically is HTTP-specific, and the other modules need not have any knowledge of what protocol is being used.

As noted above, the various modules that comprise the EWS enable the EWS to masquerade (to the SUT) as clients. As connections are opened and closed, the EWS duplicates the TCP traffic flowing through the production system. It parses the ghost TCP streams into HTTP responses, checks for equivalence (or other application-level logic validation), records mismatches for human or automated analysis, and facilitates performance analysis of the SUT or the components thereof. As noted above, the EWS (specifically, the state machine) absorbs or “black-holes” the SUT responses passed from the invisible ghosts through the collector to isolate the SUT from the real-world.

FIGS. 6-7 illustrate state changes of the state machine in response to receiving packets from the various endpoints of the connections. Normal TCP connections only have two (2) endpoints, namely, the client and the production server. In the testing infrastructure, on the contrary, three (3) endpoints exist, namely, the client, the production system server and the invisible ghost server. FIG. 6 is the opening state diagram, and FIG. 7 is the closing state diagram. This separation is for clarity and omits some possible states. For instance, the production system may start closing the connection before the invisible system has finished establishing it. In addition, the effect of reset packets is ignored for convenience as those packets are not considered part of a normal traffic flow. Familiarity with basic TCP operation is presumed. In the opening diagram (FIG. 6), the states are denoted by three (3) binary digits, a “1” in the position indicates that a particular packet has been received, and a “0” represents that it has not been received. For the opening states, the leftmost bit represents the client's first ACK, the middle bit the production server SYNACK, and the rightmost bit the invisible server SYNACK. It is assumed that the client SYN has already been received or the state machine would not be entered. There are more control packets sent as part of connection tear-down, as illustrated in the closing diagram (FIG. 7). The relevant packets examined are the invisible ghost fin (I_FIN), production server fin (P_FIN), client fin (C_FIN), and client finack of the client fin (I_ACK(C_F). Some packets that are part of the tear-down process for normal TCP connections are not relevant to the state machine. Different line types denote which packet was received that triggered the state change, and optionally what packet was sent as a result (indicated by an S( ), S(A) being an ACK, and S(F) being a FIN). Dashed lines are used for those state changes that include sending out a packet.

FIGS. 8-14 illustrate representative data generated by the testing infrastructure for a given connection. FIG. 8 illustrates the client-production server conversation for the connection. FIG. 9 illustrates how the EWS duplicates the connection open and how the invisible ghost under test responds. FIG. 10 illustrates how the EWS duplicates the client's first ack packet and the client request. FIG. 11 illustrates the production and invisible ghost responses. FIG. 12 illustrates the client acknowledgement, the EWS acknowledgement and FIN. FIG. 13 illustrates the connection close, and FIG. 14 illustrates a representative comparator report.

The present invention provides a number of new features and advantages. First, EWS enables monitoring of a production system to generate network-packet level accurate traffic that is then duplicated onto a SUT. This provides an extremely high-fidelity workload for the test system. Second, the output of the system is compared against the results of a running production system, which provides a very detailed check (if the new system is producing the desired results) without requiring the construction of a large number of test cases. Finally, the system under test is subjected to real world workload, but the system has no interactions with the outside.

The following illustrates various routines and data structures that may be used to implement the EWS modules described above:

Although the present invention has been described and illustrated in the context of testing a CDN content staging server, this is not a limitation of the present invention. One of ordinary skill in the art will recognize that systems infrastructure underlying the present invention is suitable for testing a variety of network-based systems including web servers, proxy servers, DNS name servers, web server plugins, browsers, and the like. Thus, another illustrative production environment is a web hosting environment with the system under test being any generic web server. Moreover, by adapting the test logic used to determine “equivalent output” between a production system and the SUT, real-world workloads can be used to test and validate new functionalities, regardless of the specific nature of the SUT. 

1. A method for testing software in a production environment handling live traffic between clients and at least one production server, comprising: connecting a system under test into the production environment; replicating live traffic between clients and at least one production server onto the system under test while isolating the system under test from the production environment; comparing a response from the production server with a corresponding response from the system under test to evaluate the system under test; terminating the response from the system under test before the response reaches the production environment; and responsive to the comparing step, providing a notification based on the response from the system under test.
 2. The method as described in claim 1 wherein the production environment is a content delivery network (CDN) and the system under test is a CDN content server.
 3. The method as described in claim 2 wherein the CDN comprises a set of production servers and the system under test comprises a corresponding set of content servers.
 4. The method as described in claim 1 further including the step of logging given data replicated from the live traffic.
 5. The method as described in claim 1 wherein the production environment includes a switch, and the step of replicating pulls the live traffic by port scanning the switch.
 6. The method as described in claim 1 wherein the step of comparing determines whether the system under test has given minimum functionality.
 7. The method as described in claim 1 wherein the step of comparing determines whether the response from the production server and the corresponding response from the system under test are identical.
 8. The method as described in claim 1 wherein the step of comparing determines whether the response from the production server and the corresponding response from the system under test are equivalent.
 9. A method for field-testing operative in a content delivery network (CDN) handling live traffic between clients and a set of production servers organized into a region, wherein the region includes a switch and a set of content servers under test, the method comprising: replicating traffic between clients and the production servers onto the set of content servers under test, wherein the traffic is one of: live traffic, and a recorded trace of live traffic; comparing a response from a given production server with a corresponding response from a given content server under test; selectively logging data from the comparison; and terminating the response from the content server system under test before the response reaches a requesting client.
 10. The method as described in claim 9 wherein the step of comparing determines whether the content server under test has given minimum functionality.
 11. The method as described in claim 9 wherein the step of comparing determines whether the response from the given production server and the corresponding response from the given content server are identical.
 12. The method as described in claim 9 wherein the step of comparing determines whether the response from the given production server and the corresponding response from the given content server under test are equivalent.
 13. A method for field-testing operative in a request-response production environment handling live TCP-based traffic between clients and a production server, comprising: integrating a system under test (SUT) into the production environment; using the live TCP-based traffic to generate load on the system under test (SUT); and as a given test is carried out, terminating SUT responses intended for the clients; wherein the given test parses TCP-based traffic into an application-level response and provides an application-level logic validation between the production server and the system under test (SUT).
 14. The method as described in claim 13 wherein the request-responses production environment is a content delivery network (CDN) and the production server is a CDN caching appliance.
 15. The method as described in claim 13 wherein the request-response production environment is a web hosting environment and the production server is a web server.
 16. The method as described in claim 13 wherein the application-level logic validation compares an HTTP response from the production server and the system under test (SUT).
 17. The method as described in claim 13 further including the step of logging TCP packets from the live TCP-based traffic as the given test is carried out.
 18. The method as described in claim 1 wherein the production server is one of: a web server, a proxy server, a name server, and network-based code.
 19. The method as described in claim 9 wherein the production server is one of: a web server, a proxy server, a name server, and network-based code.
 20. The method as described in claim 13 wherein the production server is one of: a web server, a proxy server, a name server, and network-based code. 